Diversity, Equity, Inclusion

The Pollard Lab and Gladstone Bioinformatics Core are committed to dismantling structural racism in the sciences and sustaining an inclusive environment for each team member to thrive.

Our core values are equity, innovation and open science principles.

Equity depends on just and fair access to opportunities and resources. We acknowledge that educational and research environments are often unwelcoming, unfair and inequitable. We are committed to doing better by maintaining a climate of mutual respect and fostering independence within the context of an interactive and supportive team. We have mentored 63 scientists over the past 12 years, 56% women and 8% from underrepresented groups. As mentors, we are committed to learning what each team member needs to succeed and teaching scientific leadership skills to carry forward.

Innovation requires curiosity, creativity and diversity of thought. We practice “design for inference”, meaning computational thinking drives scientific inquiry. Our team has produced first-in-class scientific databases, novel statistical models, and bioinformatics algorithms. We collaborate proactively with technology builders and disease experts to maximize the breadth and depth of expertise applied in our bioinformatics approaches. We seek creative problem solvers pulling on diverse experiences to complement and invigorate our team. All team members enjoy opportunities to learn more statistics, increase the scale and types of data used in analyses, develop open-source software, and to develop communication and collaboration skills.

Open science encompasses open-source code and open access data sharing, as well as active participation in changing stale paradigms in education, research and publishing. The goal is to promote greater efficiency and reproducibility in scientific research. Our team has a strong track record for open source software development that promotes inclusive, international collaboration and teaching opportunities. We write code and develop statistical models that integrate data from the public domain and emerging technologies. We see open science practices as a critical means to increasing access and participation in academic research and discovery.